Can Audio Language Models Listen Between the Lines? A Study on Metaphorical Reasoning via Unspoken

摘要

Recent advancements in Audio Language Models (ALMs) have led to significant improvements in speech-related tasks. However, their capacity for profound metaphorical reasoning, especially when derived from audio-specific cues, has yet to be thoroughly investigated. To address this gap, we introduce Unspoken, a bilingual (Chinese-English) question answering benchmark designed to assess ALMs' comprehension of non-literal, metaphor-rich audio. Unlike prior text-centric evaluations, Unspoken emphasizes prosody, phonetic ambiguity, emotional inflection, and other nuanced acoustic features critical to metaphor understanding but often lost in transcription. We construct a high-quality dataset of 2,764 manually curated and validated QA pairs, spanning three reasoning dimensions: semantic, acoustic, and contextual, and covering six common types of metaphors. Evaluation across 23 mainstream ALMs reveals a substantial performance gap: the best model achieves only 69.5% accuracy, significantly below the human average of 81.1%. By analyzing the error patterns, we identify five key failure modes that reveal fundamental limitations in current models' reasoning capabilities. Unspoken not only sets a new standard for evaluating metaphorical reasoning in audio but also pioneers a novel research direction that moves beyond transcription-based assessments. Grounding metaphor understanding in authentic human communication scenarios offers deep insight for developing more cognitively capable ALMs. The data and codes are available at https://github.com/Hongru0306/UNSPOKEN.